MSIndexVector MSIntTableColumn::rangeGradeDown(const MSIndexVector &start_,const MSIndexVector &end_)
{
if (MSView::model()!=0&&start_.length()>0&&start_.length()==end_.length())
{
MSIntVector &vector=*(MSIntVector *)_model;
MSIndexVector index(vector.length());
for (unsigned i=0;i<start_.length();i++)
{
MSIndexVector subIndex;
subIndex.series(end_(i)-start_(i)+1,start_(i));
MSIntVector subVector=MSIntVector::select(vector,subIndex);
MSIndexVector sortedIndex=subVector.gradeDown();
unsigned startIndex=start_(i);
for (unsigned j=0;j<sortedIndex.length();j++)
{
index.replaceAt(startIndex+j,sortedIndex(j)+startIndex);
}
}
return index;
}
else return MSIndexVector::nullVector();
}
int main (int argc, char **argv) {
IloEnv env;
try {
IloCplex cplex(env);
IloTimer timer(env);
const IloNum startTime = cplex.getCplexTime();
parseCommandLine(argc, argv);
printCommandLine(argc, argv);
// set all default constraint weights as zero
consWts["NonOperatorsAtMostOnce"] = 0;
consWts["StackDepthUpperBound"] = 0;
consWts["ExactlyOneUnknown"] = 0;
consWts["NoTwoConsecutiveMultiplications"] = 0; // note: this may be disabled if word "dozen" appears
consWts["NoTwoConsecutiveDivisions"] = 0;
consWts["NoConsecutiveMultAndDiv"] = 0;
consWts["NoNegatives"] = 0;
consWts["TypeConsistency"] = 0;
consWts["EqualityFirstOrLast"] = 0;
consWts["IntConstantsImplyIntUnknown"] = 0;
consWts["PreserveOrderingInText"] = 0;
consWts["UnknownFirstOrLast"] = 0;
consWts["EqualityNextToUnknown"] = 0;
consWts["HasAddition"] = 0;
consWts["HasSubtraction"] = 0;
consWts["HasMultiplication"] = 0;
consWts["HasDivision"] = 0;
// parse config file defining constraint weights
parseConfigFile(param_wts_config_file);
cout << "Starting IloTimer" << endl;
timer.start();
// parse arithmetic model parameters; note: this may override previously
// set constraint weights
if (strlen(arith_filename) > 0)
parseInputFile(arith_filename);
// set cplex paramters
setCplexParameters(cplex);
// import or build the model
IloModel model(env);
IloObjective obj(env);
IloNumVarArray corevars(env);
IloRangeArray rngs(env);
if (strlen(mip_filename) > 0) {
cout << "------- Importing the model -------" << endl;
cplex.importModel(model, mip_filename, obj, corevars, rngs);
cout << "Model imported at " << (cplex.getCplexTime() - startTime) << " sec" << endl;
}
else {
cout << "------- Building the model -------" << endl;
buildArithmeticModel(model, obj, corevars, rngs);
}
int nvars = corevars.getSize();
cout << "Number of Core Variables: " << nvars << endl;
cplex.extract(model);
cout << "Model extracted at " << (cplex.getCplexTime() - startTime) << " sec" << endl;
// save the MIP model to a file, if desired
if (!param_savemodel.empty()) {
cout << endl << "Saving generated MIP model to " << param_savemodel << endl << endl;
cplex.exportModel(param_savemodel.c_str());
}
// find out whether it is a minimization problem or a maximization one
bool isMinimization = true;
if (cplex.getObjective().getSense() == IloObjective::Maximize)
isMinimization = false;
// ask cplex to use MIPInfoCallback
cplex.use(MIPInfoCallback(env, isMinimization, cplex, startTime));
cout << "------- Solving the extracted model -------" << endl;
//const bool solutionFound = cplex.solve();
const bool solutionFound = (param_nsolutions > 1 ? cplex.populate() : cplex.solve());
const int nSolutionsFound = cplex.getSolnPoolNsolns();
cout << "Stopping IloTimer" << endl;
timer.stop();
const IloNum endTime = cplex.getCplexTime();
cout << "-------------------------------------------" << endl;
printCommandLine(argc, argv);
cout << "-------------------------------------------" << endl;
cout << "Number of cplex nodes = " << cplex.getNnodes() << endl;
cout << "Number of cplex iterations = " << cplex.getNiterations() << endl;
cout << "Aggregated CPU time = " << timer.getTime() << " seconds" << endl;
cout << "Elapsed wall clock time = " << endTime - startTime << " seconds" << endl;
cout << "Number of threads used = " << param_threads << endl;
cout << "Solution status = " << cplex.getStatus() << endl;
if (solutionFound) {
cout << "Solution value = " << cplex.getObjValue() << endl;
cout << "Optimality Gap (in %) = " << fabs((cplex.getBestObjValue() - cplex.getObjValue()) / (1.0 * cplex.getObjValue())) * 100 << endl;
//cout << "Maximum bound violation = " << cplex.getQuality(IloCplex::MaxPrimalInfeas) << endl;
}
cout << endl << "parameters: n=" << n << " l=" << l << " k=" << k << " p=" << p << " q=" << q << " m=" << m << endl;
if (solutionFound) {
cout << "TOTAL " << nSolutionsFound << " solutions found" << endl;
int nAllowedSolutionsFound = 0;
if (param_printexpr || param_printanswer || param_printsoln) {
IloNumArray objValues(env, nSolutionsFound);
vector<pair<IloNum,unsigned> > sortedIndex(nSolutionsFound); // to sort solutions by objective value
vector<FormattedExpr> expressions(nSolutionsFound);
// extract all solutions as formatted expressions
for (int s=0; s<nSolutionsFound; s++) {
IloNumArray vals(env);
cplex.getValues(vals, corevars, s);
// convert solution values to integers; note: simple int cast may lead to errors!
IloIntArray intvals(env, vals.getSize());
for (int i=0; i<vals.getSize(); i++)
intvals[i] = IloRound(vals[i]); // use IloRound rather than std::round
if (param_printsoln)
prettyPrintSoln(intvals);
objValues[s] = cplex.getObjValue(s);
expressions[s] = getFormattedExpr(intvals);
sortedIndex[s] = pair<IloNum,unsigned> (objValues[s],s);
}
// sort solutions by increasing objective value
std::stable_sort(sortedIndex.begin(), sortedIndex.end());
// identify which expressions are unique (ignoring type differences);
// prefer to keep those that appear earlier in the above sorted order
set<int> uniqueExprIndices;
set<string> seenExpressions;
if (!param_allowdupes) {
for (int s=0; s<nSolutionsFound; s++) {
const int sId = sortedIndex[s].second;
const string & exprPf = expressions[sId].postfix;
if (seenExpressions.find(exprPf) == seenExpressions.end()) {
uniqueExprIndices.insert(sId);
seenExpressions.insert(exprPf);
}
}
}
// evaluate all expressions with a single call to Python's SymPy package
if (param_printanswer)
solveExpressionsWithSymPy(expressions);
// print expressions if desired, in sorted order
if (param_printexpr) {
cout << "SOLN: CORRECT | POS/NEG | INT/FRA | OBJ-SCORE | TRUE-ANS | ANS | INFIX | POSTFIX | TYPED-POSTFIX" << endl;
for (int s=0; s<nSolutionsFound; s++) {
const int sId = sortedIndex[s].second;
if (!param_allowdupes && uniqueExprIndices.find(sId) == uniqueExprIndices.end())
continue;
const FormattedExpr & expr = expressions[sId];
const double answerValue = atof(expr.answer.c_str());
const bool isCorrect = (fabs(answerValue - trueAnswer) < epsilon);
const bool isAnswerNegative = answerValue < 0;
const bool isAnswerInteger = isInt(answerValue);
if (!isAnswerNegative && (!allIntConstants || consWts["IntConstantsImplyIntUnknown"] == 0 || isAnswerInteger)) {
++nAllowedSolutionsFound;
cout << "EXPR: " << isCorrect
<< " | " << (isAnswerNegative ? "NEG" : "POS")
<< " | " << (isAnswerInteger ? "INT" : "FRA")
<< " | " << objValues[sId]
<< " | " << trueAnswer
<< " | " << expr.answer
<< " | " << expr.infix
<< " | " << expr.postfix
<< " | " << expr.typedPostfix
<< endl;
}
}
}
}
const string solnProperty = (param_allowdupes ? "" : " unique,")
+ string(" non-negative")
+ (allIntConstants && consWts["IntConstantsImplyIntUnknown"] != 0 ? ", integer-valued " : " ");
cout << "NET " << nAllowedSolutionsFound << solnProperty << "solutions found out of "
<< nSolutionsFound << " total solutions" << endl;
}
cout << "-------------------------------------------" << endl;
cout << "RESULT:"
<< " NODES " << cplex.getNnodes()
<< " | ITERATIONS " << cplex.getNiterations()
<< " | CPUTIME " << timer.getTime()
<< " | WALLTIME " << endTime - startTime
<< " | THREADS " << param_threads
<< " | STATUS " << cplex.getStatus();
if (solutionFound)
cout << " | SOLUTION " << cplex.getObjValue()
<< " | OPTGAP " << fabs((cplex.getBestObjValue() - cplex.getObjValue()) / (1.0 * cplex.getObjValue())) * 100;
else
cout << " | SOLUTION - | OPTGAP -";
cout << endl;
//try { // basis may not exist
// IloCplex::BasisStatusArray cstat(env);
// cplex.getBasisStatuses(cstat, vars);
// cout << "Basis statuses = " << cstat << endl;
//}
//catch (...) {
//}
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end();
return 0;
} // END main
int
MilpRounding::solution(double &solutionValue, double *betterSolution)
{
if(model_->getCurrentPassNumber() > 1) return 0;
if (model_->currentDepth() > 2 && (model_->getNodeCount()%howOften_)!=0)
return 0;
int returnCode = 0; // 0 means it didn't find a feasible solution
OsiTMINLPInterface * nlp = NULL;
if(setup_->getAlgorithm() == B_BB)
nlp = dynamic_cast<OsiTMINLPInterface *>(model_->solver()->clone());
else
nlp = dynamic_cast<OsiTMINLPInterface *>(setup_->nonlinearSolver()->clone());
TMINLP2TNLP* minlp = nlp->problem();
// set tolerances
double integerTolerance = model_->getDblParam(CbcModel::CbcIntegerTolerance);
//double primalTolerance = 1.0e-6;
int n;
int m;
int nnz_jac_g;
int nnz_h_lag;
Ipopt::TNLP::IndexStyleEnum index_style;
minlp->get_nlp_info(n, m, nnz_jac_g,
nnz_h_lag, index_style);
const Bonmin::TMINLP::VariableType* variableType = minlp->var_types();
const double* x_sol = minlp->x_sol();
const double* g_l = minlp->g_l();
const double* g_u = minlp->g_u();
const double * colsol = model_->solver()->getColSolution();
// Get information about the linear and nonlinear part of the instance
TMINLP* tminlp = nlp->model();
vector<Ipopt::TNLP::LinearityType> c_lin(m);
tminlp->get_constraints_linearity(m, c_lin());
vector<int> c_idx(m);
int n_lin = 0;
for (int i=0;i<m;i++) {
if (c_lin[i]==Ipopt::TNLP::LINEAR)
c_idx[i] = n_lin++;
else
c_idx[i] = -1;
}
// Get the structure of the jacobian
vector<int> indexRow(nnz_jac_g);
vector<int> indexCol(nnz_jac_g);
minlp->eval_jac_g(n, x_sol, false,
m, nnz_jac_g,
indexRow(), indexCol(), 0);
// get the jacobian values
vector<double> jac_g(nnz_jac_g);
minlp->eval_jac_g(n, x_sol, false,
m, nnz_jac_g,
NULL, NULL, jac_g());
// Sort the matrix to column ordered
vector<int> sortedIndex(nnz_jac_g);
CoinIotaN(sortedIndex(), nnz_jac_g, 0);
MatComp c;
c.iRow = indexRow();
c.jCol = indexCol();
std::sort(sortedIndex.begin(), sortedIndex.end(), c);
vector<int> row (nnz_jac_g);
vector<double> value (nnz_jac_g);
vector<int> columnStart(n,0);
vector<int> columnLength(n,0);
int indexCorrection = (index_style == Ipopt::TNLP::C_STYLE) ? 0 : 1;
int iniCol = -1;
int nnz = 0;
for(int i=0; i<nnz_jac_g; i++) {
int thisIndexCol = indexCol[sortedIndex[i]]-indexCorrection;
int thisIndexRow = c_idx[indexRow[sortedIndex[i]] - indexCorrection];
if(thisIndexCol != iniCol) {
iniCol = thisIndexCol;
columnStart[thisIndexCol] = nnz;
columnLength[thisIndexCol] = 0;
}
if(thisIndexRow == -1) continue;
columnLength[thisIndexCol]++;
row[nnz] = thisIndexRow;
value[nnz] = jac_g[i];
nnz++;
}
// Build the row lower and upper bounds
vector<double> newRowLower(n_lin);
vector<double> newRowUpper(n_lin);
for(int i = 0 ; i < m ; i++){
if(c_idx[i] == -1) continue;
newRowLower[c_idx[i]] = g_l[i];
newRowUpper[c_idx[i]] = g_u[i];
}
// Get solution array for heuristic solution
vector<double> newSolution(n);
std::copy(x_sol, x_sol + n, newSolution.begin());
// Define the constraint matrix for MILP
CoinPackedMatrix matrix(true,n_lin,n, nnz, value(), row(), columnStart(), columnLength());
// create objective function and columns lower and upper bounds for MILP
// and create columns for matrix in MILP
//double alpha = 0;
double beta = 1;
vector<double> objective(n);
vector<int> idxIntegers;
idxIntegers.reserve(n);
for(int i = 0 ; i < n ; i++){
if(variableType[i] != Bonmin::TMINLP::CONTINUOUS){
idxIntegers.push_back(i);
objective[i] = beta*(1 - 2*colsol[i]);
}
}
#if 0
// Get dual multipliers and build gradient of the lagrangean
const double * duals = nlp->getRowPrice() + 2 *n;
vector<double> grad(n, 0);
vector<int> indices(n, 0);
tminlp->eval_grad_f(n, x_sol, false, grad());
for(int i = 0 ; i < m ; i++){
if(c_lin[i] == Ipopt::TNLP::LINEAR) continue;
int nnz;
tminlp->eval_grad_gi(n, x_sol, false, i, nnz, indices(), NULL);
tminlp->eval_grad_gi(n, x_sol, false, i, nnz, NULL, grad());
for(int k = 0 ; k < nnz ; k++){
objective[indices[k]] += alpha *duals[i] * grad[k];
}
}
for(int i = 0 ; i < n ; i++){
if(variableType[i] != Bonmin::TMINLP::CONTINUOUS)
objective[i] += alpha * grad[i];
//if(fabs(objective[i]) < 1e-4) objective[i] = 0;
else objective[i] = 0;
}
std::copy(objective.begin(), objective.end(), std::ostream_iterator<double>(std::cout, " "));
std::cout<<std::endl;
#endif
// load the problem to OSI
OsiSolverInterface *si = mip_->solver();
assert(si != NULL);
CoinMessageHandler * handler = model_->messageHandler()->clone();
si->passInMessageHandler(handler);
si->messageHandler()->setLogLevel(1);
si->loadProblem(matrix, model_->solver()->getColLower(), model_->solver()->getColUpper(), objective(),
newRowLower(), newRowUpper());
si->setInteger(idxIntegers(), static_cast<int>(idxIntegers.size()));
si->applyCuts(noGoods);
bool hasFractionnal = true;
while(hasFractionnal){
mip_->optimize(DBL_MAX, 0, 60);
hasFractionnal = false;
#if 0
bool feasible = false;
if(mip_->getLastSolution()) {
const double* solution = mip_->getLastSolution();
std::copy(solution, solution + n, newSolution.begin());
feasible = true;
}
if(feasible) {
// fix the integer variables and solve the NLP
// also add no good cut
CoinPackedVector v;
double lb = 1;
for (int iColumn=0;iColumn<n;iColumn++) {
if (variableType[iColumn] != Bonmin::TMINLP::CONTINUOUS) {
double value=newSolution[iColumn];
if (fabs(floor(value+0.5)-value)>integerTolerance) {
#ifdef DEBUG_BON_HEURISTIC_DIVE_MIP
cout<<"It should be infeasible because: "<<endl;
cout<<"variable "<<iColumn<<" is not integer"<<endl;
#endif
feasible = false;
break;
}
else {
value=floor(newSolution[iColumn]+0.5);
if(value > 0.5){
v.insert(iColumn, -1);
lb -= value;
}
minlp->SetVariableUpperBound(iColumn, value);
minlp->SetVariableLowerBound(iColumn, value);
}
}
}
}
#endif
}
bool feasible = false;
if(mip_->getLastSolution()) {
const double* solution = mip_->getLastSolution();
std::copy(solution, solution + n, newSolution.begin());
feasible = true;
delete handler;
}
if(feasible) {
// fix the integer variables and solve the NLP
// also add no good cut
CoinPackedVector v;
double lb = 1;
for (int iColumn=0;iColumn<n;iColumn++) {
if (variableType[iColumn] != Bonmin::TMINLP::CONTINUOUS) {
double value=newSolution[iColumn];
if (fabs(floor(value+0.5)-value)>integerTolerance) {
feasible = false;
break;
}
else {
value=floor(newSolution[iColumn]+0.5);
if(value > 0.5){
v.insert(iColumn, -1);
lb -= value;
}
minlp->SetVariableUpperBound(iColumn, value);
minlp->SetVariableLowerBound(iColumn, value);
}
}
}
OsiRowCut c;
c.setRow(v);
c.setLb(lb);
c.setUb(DBL_MAX);
noGoods.insert(c);
if(feasible) {
nlp->initialSolve();
if(minlp->optimization_status() != Ipopt::SUCCESS) {
feasible = false;
}
std::copy(x_sol,x_sol+n, newSolution.begin());
}
}
if(feasible) {
double newSolutionValue;
minlp->eval_f(n, newSolution(), true, newSolutionValue);
if(newSolutionValue < solutionValue) {
std::copy(newSolution.begin(), newSolution.end(), betterSolution);
solutionValue = newSolutionValue;
returnCode = 1;
}
}
delete nlp;
#ifdef DEBUG_BON_HEURISTIC_DIVE_MIP
std::cout<<"DiveMIP returnCode = "<<returnCode<<std::endl;
#endif
return returnCode;
}
